{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3cf5bd64",
   "metadata": {},
   "source": [
    "### 9.3\t案例：对iris数据集进行决策树分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb899bb",
   "metadata": {},
   "source": [
    "#### 9.3.1 构建决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfe144c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树在测试集上的准确率: 1.0\n"
     ]
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 加载iris数据集\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:]  # 使用petal length (cm)和petal width (cm)作为自变量\n",
    "y = iris.target  # 因变量\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建决策树模型\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# 在训练集上训练模型\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"决策树在测试集上的准确率: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bdc2695",
   "metadata": {},
   "source": [
    "#### 9.3.2 查看生成的决策规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0133351f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树规则:\n",
      " |--- petal length (cm) <= 2.45\n",
      "|   |--- class: 0\n",
      "|--- petal length (cm) >  2.45\n",
      "|   |--- petal length (cm) <= 4.75\n",
      "|   |   |--- petal width (cm) <= 1.65\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- petal width (cm) >  1.65\n",
      "|   |   |   |--- class: 2\n",
      "|   |--- petal length (cm) >  4.75\n",
      "|   |   |--- petal width (cm) <= 1.75\n",
      "|   |   |   |--- petal length (cm) <= 4.95\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- petal length (cm) >  4.95\n",
      "|   |   |   |   |--- petal width (cm) <= 1.55\n",
      "|   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |--- petal width (cm) >  1.55\n",
      "|   |   |   |   |   |--- petal length (cm) <= 5.45\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- petal length (cm) >  5.45\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |--- petal width (cm) >  1.75\n",
      "|   |   |   |--- petal length (cm) <= 4.85\n",
      "|   |   |   |   |--- class: 2\n",
      "|   |   |   |--- petal length (cm) >  4.85\n",
      "|   |   |   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_text\n",
    "\n",
    "# 使用 export_text 函数打印决策树规则\n",
    "tree_rules = export_text(model, feature_names=[\"petal length (cm)\", \"petal width (cm)\"])\n",
    "print(\"决策树规则:\\n\", tree_rules)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e56fba",
   "metadata": {},
   "source": [
    "#### 9.3.3 决策树可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9ca1b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'iris_decision_tree.pdf'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "\n",
    "# 导出决策树结构为DOT格式\n",
    "dot_data = export_graphviz(model, out_file=None, \n",
    "                           feature_names=[\"petal length (cm)\", \"petal width (cm)\"],  \n",
    "                           class_names=iris.target_names,  \n",
    "                           filled=True, rounded=True,  \n",
    "                           special_characters=True)  \n",
    "\n",
    "# 使用Graphviz库可视化DOT格式的决策树\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"iris_decision_tree\")  # 可选：保存可视化结果到文件\n",
    "graph.view(\"iris_decision_tree\")    # 打开可视化结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "706711e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看符合此规则训练样本的数量：\n",
      " 40\n",
      "查看符合此规则训练样本的实际标签：\n",
      " ['setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print('查看符合此规则训练样本的数量：\\n',np.sum(X_train[:,0]<=2.45))\n",
    "print('查看符合此规则训练样本的实际标签：\\n',iris.target_names[y_train[X_train[:,0]<=2.45]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce0a9dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建图形\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# 使用 plot_tree 函数进行可视化\n",
    "plot_tree(model, feature_names=[\"petal length (cm)\", \"petal width (cm)\"], \n",
    "          class_names=iris.target_names, filled=True, rounded=True)\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d085a99d",
   "metadata": {},
   "source": [
    "#### 9.3.4 绘制决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6a2f25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=1, random_state=42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建深度为1的决策树模型\n",
    "clf = DecisionTreeClassifier(max_depth=1,random_state=42)\n",
    "# 在训练集上训练模型\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "25b85b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "# 使用之前训练好的模型和数据集绘制决策边界\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_decision_regions(X_test, y_test, clf=clf, legend=2)\n",
    "\n",
    "# 设置图形属性\n",
    "plt.xlabel('Petal Length (cm)')\n",
    "plt.ylabel('Petal Width (cm)')\n",
    "plt.title('Decision Boundary using mlxtend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "281ddf84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 构建深度为1的决策树模型\n",
    "clf = DecisionTreeClassifier(max_depth=2,random_state=42)\n",
    "# 在训练集上训练模型\n",
    "clf.fit(X_train, y_train)\n",
    "# 使用之前训练好的模型和数据集绘制决策边界\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_decision_regions(X_test, y_test, clf=clf,legend=2)\n",
    "\n",
    "# 设置图形属性\n",
    "plt.xlabel('Petal Length (cm)')\n",
    "plt.ylabel('Petal Width (cm)')\n",
    "plt.title('Decision Boundary using mlxtend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "53f5c398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树规则:\n",
      " |--- petal length (cm) <= 2.45\n",
      "|   |--- class: 0\n",
      "|--- petal length (cm) >  2.45\n",
      "|   |--- petal length (cm) <= 4.75\n",
      "|   |   |--- class: 1\n",
      "|   |--- petal length (cm) >  4.75\n",
      "|   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用 export_text 函数打印决策树规则\n",
    "tree_rules = export_text(clf, feature_names=[\"petal length (cm)\", \"petal width (cm)\"])\n",
    "print(\"决策树规则:\\n\", tree_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "914b452d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集预测结果的混淆矩阵：\n",
      " [[10  0  0]\n",
      " [ 0  8  1]\n",
      " [ 0  0 11]]\n"
     ]
    }
   ],
   "source": [
    "# 查看对测试集预测结果的混淆菊展\n",
    "from sklearn.metrics import confusion_matrix \n",
    "y_pred = clf.predict(X_test)\n",
    "print('测试集预测结果的混淆矩阵：\\n',confusion_matrix (y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e29b60",
   "metadata": {},
   "source": [
    "### 9.4\t案例：对乳腺癌数据集进行决策树分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24be1a29",
   "metadata": {},
   "source": [
    "#### 9.4.1 数据理解及处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "233ef895",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载所需的三方库\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# 读取数据集\n",
    "data = pd.read_csv('data/breast_cancer_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a269dee5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看数据形状：\n",
      " (569, 33)\n",
      "查看各列的数据类型：\n",
      " id                           int64\n",
      "diagnosis                   object\n",
      "radius_mean                float64\n",
      "texture_mean               float64\n",
      "perimeter_mean             float64\n",
      "area_mean                  float64\n",
      "smoothness_mean            float64\n",
      "compactness_mean           float64\n",
      "concavity_mean             float64\n",
      "concave points_mean        float64\n",
      "symmetry_mean              float64\n",
      "fractal_dimension_mean     float64\n",
      "radius_se                  float64\n",
      "texture_se                 float64\n",
      "perimeter_se               float64\n",
      "area_se                    float64\n",
      "smoothness_se              float64\n",
      "compactness_se             float64\n",
      "concavity_se               float64\n",
      "concave points_se          float64\n",
      "symmetry_se                float64\n",
      "fractal_dimension_se       float64\n",
      "radius_worst               float64\n",
      "texture_worst              float64\n",
      "perimeter_worst            float64\n",
      "area_worst                 float64\n",
      "smoothness_worst           float64\n",
      "compactness_worst          float64\n",
      "concavity_worst            float64\n",
      "concave points_worst       float64\n",
      "symmetry_worst             float64\n",
      "fractal_dimension_worst    float64\n",
      "Unnamed: 32                float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 查看数据形状\n",
    "print('查看数据形状：\\n',data.shape)\n",
    "# 查看各列的数据类型\n",
    "print('查看各列的数据类型：\\n',data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1f7ab661",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分特征和目标变量\n",
    "X = data.drop(['id', 'diagnosis', 'Unnamed: 32'], axis=1)  # 删除不需要的列\n",
    "y = data['diagnosis']  # 目标变量为 'diagnosis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ddcdae32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看y的类别数量\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "63ee711e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将目标变量编码为数值（Malignant为1，Benign为0）\n",
    "y = y.map({'M': 1, 'B': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "053cd84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1e1213f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立决策树模型\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bedc2fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'breast-cancer_decision_tree.pdf'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 决策树可视化\n",
    "# 导出决策树结构为DOT格式\n",
    "dot_data = export_graphviz(model, out_file=None, \n",
    "                           feature_names=X.columns,  \n",
    "                           class_names=['B','M'],  \n",
    "                           filled=True, rounded=True,  \n",
    "                           special_characters=True)  \n",
    "\n",
    "# 使用Graphviz库可视化DOT格式的决策树\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"breast-cancer_decision_tree\")  # 可选：保存可视化结果到文件\n",
    "graph.view(\"breast-cancer_decision_tree\")    # 打开可视化结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "19a06bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95\n",
      "Confusion Matrix:\n",
      "[[68  3]\n",
      " [ 3 40]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96        71\n",
      "           1       0.93      0.93      0.93        43\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.94      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上进行预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 评估模型性能\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# 打印混淆矩阵和分类报告\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f'Confusion Matrix:\\n{conf_matrix}')\n",
    "\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(f'Classification Report:\\n{class_report}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4b1168",
   "metadata": {},
   "source": [
    "#### 9.4.3 使用网格搜索寻找最优决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f2d7a834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "Accuracy of Best Model: 0.96\n",
      "Classification Report of Best Model:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97        71\n",
      "           1       1.00      0.91      0.95        43\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.97      0.95      0.96       114\n",
      "weighted avg       0.97      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 定义决策树模型\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# 定义网格搜索的超参数组合\n",
    "param_grid = {\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'criterion': ['gini', 'entropy']\n",
    "}\n",
    "\n",
    "# 使用GridSearchCV进行网格搜索\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数组合\n",
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "\n",
    "# 获取最优模型\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# 评估最优模型性能\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy of Best Model: {accuracy:.2f}')\n",
    "\n",
    "# 打印最优模型的分类报告\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(f'Classification Report of Best Model:\\n{class_report}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f50a49f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵:\n",
      "[[71  0]\n",
      " [ 4 39]]\n"
     ]
    }
   ],
   "source": [
    "# 打印混淆矩阵\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f'混淆矩阵:\\n{conf_matrix}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e1130e",
   "metadata": {},
   "source": [
    "### 9.5\t 随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f2f224",
   "metadata": {},
   "source": [
    "#### 9.5.3案例：对乳腺癌数据集进行随机森林分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c4e568ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.96\n",
      "Confusion Matrix:\n",
      "[[70  1]\n",
      " [ 3 40]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97        71\n",
      "           1       0.98      0.93      0.95        43\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.97      0.96      0.96       114\n",
      "weighted avg       0.97      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# 建立随机森林模型\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# 评估模型性能\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# 打印混淆矩阵和分类报告\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f'Confusion Matrix:\\n{conf_matrix}')\n",
    "\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(f'Classification Report:\\n{class_report}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f981c1",
   "metadata": {},
   "source": [
    "#### 9.5.4使用网格搜索寻找最优随机森林模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "582078c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Accuracy of Best Model: 0.96\n"
     ]
    }
   ],
   "source": [
    "# 定义随机森林模型\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# 定义网格参数\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [None, 5, 10, 20],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# 使用GridSearchCV进行网格搜索\n",
    "grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数组合\n",
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "\n",
    "# 获取最优模型\n",
    "best_rf_model = grid_search.best_estimator_\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = best_rf_model.predict(X_test)\n",
    "\n",
    "# 评估最优模型性能\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy of Best Model: {accuracy:.2f}')"
   ]
  }
 ],
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